Overview

Dataset statistics

Number of variables38
Number of observations139809
Missing cells1844
Missing cells (%)< 0.1%
Duplicate rows4903
Duplicate rows (%)3.5%
Total size in memory40.5 MiB
Average record size in memory304.0 B

Variable types

Numeric6
Categorical32

Alerts

Dataset has 4903 (3.5%) duplicate rowsDuplicates
Age is highly overall correlated with EL_primary and 7 other fieldsHigh correlation
EL_higherprofessional_university is highly overall correlated with EL_secondary_higherHigh correlation
EL_primary is highly overall correlated with Age and 1 other fieldsHigh correlation
EL_secondary_higher is highly overall correlated with EL_higherprofessional_universityHigh correlation
Ethn_dutch is highly overall correlated with Ethn_nonwestern and 1 other fieldsHigh correlation
Ethn_nonwestern is highly overall correlated with Ethn_dutchHigh correlation
Ethn_western is highly overall correlated with Ethn_dutchHigh correlation
HHC_couple is highly overall correlated with Age and 1 other fieldsHigh correlation
HHC_couple_with_children is highly overall correlated with Age and 1 other fieldsHigh correlation
Main_moti_pickupdropoff_goods is highly overall correlated with Main_moti_sparetimeHigh correlation
Main_moti_sparetime is highly overall correlated with Main_moti_pickupdropoff_goodsHigh correlation
Moti_pickupdropoff_goods is highly overall correlated with Moti_sparetimeHigh correlation
Moti_sparetime is highly overall correlated with Moti_pickupdropoff_goodsHigh correlation
PW_no is highly overall correlated with Age and 3 other fieldsHigh correlation
PW_yesmorethan30h is highly overall correlated with Age and 2 other fieldsHigh correlation
UO_benefits is highly overall correlated with Age and 2 other fieldsHigh correlation
UO_none is highly overall correlated with Age and 3 other fieldsHigh correlation
UO_student/scholar is highly overall correlated with Age and 1 other fieldsHigh correlation
PW_yeslessthan12h is highly imbalanced (75.9%)Imbalance
HHC_oneperson_with_children is highly imbalanced (65.6%)Imbalance
Ethn_western is highly imbalanced (52.4%)Imbalance
Ethn_nonwestern is highly imbalanced (51.5%)Imbalance
Moti_profession is highly imbalanced (89.3%)Imbalance
Moti_pickupdropoff_person is highly imbalanced (65.5%)Imbalance
Main_moti_profession is highly imbalanced (81.4%)Imbalance
Main_moti_pickupdropoff_person is highly imbalanced (73.2%)Imbalance
Starting_postalcode has 1844 (1.3%) missing valuesMissing
Number_of_cars_in_HH has 20674 (14.8%) zerosZeros

Reproduction

Analysis started2024-07-05 11:47:26.899625
Analysis finished2024-07-05 11:48:06.004849
Duration39.11 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

Starting_postalcode
Real number (ℝ)

MISSING 

Distinct3605
Distinct (%)2.6%
Missing1844
Missing (%)1.3%
Infinite0
Infinite (%)0.0%
Mean4383.9146
Minimum1011
Maximum9991
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2024-07-05T13:48:06.330013image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum1011
5-th percentile1121
Q12571
median3743
Q36213
95-th percentile8911
Maximum9991
Range8980
Interquartile range (IQR)3642

Descriptive statistics

Standard deviation2351.6036
Coefficient of variation (CV)0.53641637
Kurtosis-0.69287706
Mean4383.9146
Median Absolute Deviation (MAD)1609
Skewness0.5542487
Sum6.0482678 × 108
Variance5530039.3
MonotonicityNot monotonic
2024-07-05T13:48:06.692927image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3011 582
 
0.4%
3511 508
 
0.4%
3512 361
 
0.3%
3584 325
 
0.2%
2492 325
 
0.2%
3991 302
 
0.2%
3012 298
 
0.2%
2496 293
 
0.2%
1012 276
 
0.2%
3061 266
 
0.2%
Other values (3595) 134429
96.2%
(Missing) 1844
 
1.3%
ValueCountFrequency (%)
1011 147
0.1%
1012 276
0.2%
1013 151
0.1%
1014 55
 
< 0.1%
1015 118
0.1%
1016 147
0.1%
1017 180
0.1%
1018 251
0.2%
1019 180
0.1%
1021 37
 
< 0.1%
ValueCountFrequency (%)
9991 9
 
< 0.1%
9989 15
< 0.1%
9988 19
< 0.1%
9987 2
 
< 0.1%
9984 2
 
< 0.1%
9983 11
 
< 0.1%
9982 15
< 0.1%
9981 35
< 0.1%
9979 2
 
< 0.1%
9978 2
 
< 0.1%

Gender_male
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
70222 
1
69587 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters139809
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 70222
50.2%
1 69587
49.8%

Length

2024-07-05T13:48:06.899798image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-05T13:48:07.050403image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 70222
50.2%
1 69587
49.8%

Most occurring characters

ValueCountFrequency (%)
0 70222
50.2%
1 69587
49.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 139809
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 70222
50.2%
1 69587
49.8%

Most occurring scripts

ValueCountFrequency (%)
Common 139809
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 70222
50.2%
1 69587
49.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 139809
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 70222
50.2%
1 69587
49.8%

Age
Real number (ℝ)

HIGH CORRELATION 

Distinct91
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43.679942
Minimum6
Maximum97
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2024-07-05T13:48:07.248018image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile12
Q128
median44
Q359
95-th percentile76
Maximum97
Range91
Interquartile range (IQR)31

Descriptive statistics

Standard deviation19.737716
Coefficient of variation (CV)0.45187139
Kurtosis-0.9047274
Mean43.679942
Median Absolute Deviation (MAD)16
Skewness0.04517761
Sum6106849
Variance389.57743
MonotonicityNot monotonic
2024-07-05T13:48:07.466392image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
51 2719
 
1.9%
50 2648
 
1.9%
53 2590
 
1.9%
54 2518
 
1.8%
49 2511
 
1.8%
52 2445
 
1.7%
46 2423
 
1.7%
47 2370
 
1.7%
48 2357
 
1.7%
28 2350
 
1.7%
Other values (81) 114878
82.2%
ValueCountFrequency (%)
6 822
0.6%
7 1141
0.8%
8 1078
0.8%
9 1184
0.8%
10 1170
0.8%
11 1342
1.0%
12 1371
1.0%
13 1274
0.9%
14 1353
1.0%
15 1317
0.9%
ValueCountFrequency (%)
97 3
 
< 0.1%
95 10
 
< 0.1%
94 8
 
< 0.1%
93 2
 
< 0.1%
92 23
 
< 0.1%
91 17
 
< 0.1%
90 48
 
< 0.1%
89 75
0.1%
88 151
0.1%
87 147
0.1%

Number_of_cars_in_HH
Real number (ℝ)

ZEROS 

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.36439
Minimum0
Maximum10
Zeros20674
Zeros (%)14.8%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2024-07-05T13:48:07.666603image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q32
95-th percentile3
Maximum10
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.0050483
Coefficient of variation (CV)0.73662833
Kurtosis14.674446
Mean1.36439
Median Absolute Deviation (MAD)1
Skewness2.2624281
Sum190754
Variance1.0101221
MonotonicityNot monotonic
2024-07-05T13:48:07.833489image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
1 65679
47.0%
2 41644
29.8%
0 20674
 
14.8%
3 8736
 
6.2%
4 1997
 
1.4%
5 457
 
0.3%
10 346
 
0.2%
6 168
 
0.1%
7 56
 
< 0.1%
9 30
 
< 0.1%
ValueCountFrequency (%)
0 20674
 
14.8%
1 65679
47.0%
2 41644
29.8%
3 8736
 
6.2%
4 1997
 
1.4%
5 457
 
0.3%
6 168
 
0.1%
7 56
 
< 0.1%
8 22
 
< 0.1%
9 30
 
< 0.1%
ValueCountFrequency (%)
10 346
 
0.2%
9 30
 
< 0.1%
8 22
 
< 0.1%
7 56
 
< 0.1%
6 168
 
0.1%
5 457
 
0.3%
4 1997
 
1.4%
3 8736
 
6.2%
2 41644
29.8%
1 65679
47.0%

Ebike_in_HH
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
100887 
1
38922 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters139809
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 100887
72.2%
1 38922
 
27.8%

Length

2024-07-05T13:48:08.033686image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-05T13:48:08.184805image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 100887
72.2%
1 38922
 
27.8%

Most occurring characters

ValueCountFrequency (%)
0 100887
72.2%
1 38922
 
27.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 139809
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 100887
72.2%
1 38922
 
27.8%

Most occurring scripts

ValueCountFrequency (%)
Common 139809
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 100887
72.2%
1 38922
 
27.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 139809
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 100887
72.2%
1 38922
 
27.8%
Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.9829196
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2024-07-05T13:48:08.334291image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q15
median8
Q39
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.6582109
Coefficient of variation (CV)0.38067328
Kurtosis-0.57339003
Mean6.9829196
Median Absolute Deviation (MAD)2
Skewness-0.68332019
Sum976275
Variance7.066085
MonotonicityNot monotonic
2024-07-05T13:48:08.499526image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
10 29085
20.8%
9 23316
16.7%
8 19290
13.8%
7 15997
11.4%
6 13174
9.4%
5 11017
 
7.9%
4 9331
 
6.7%
3 7181
 
5.1%
1 6550
 
4.7%
2 4868
 
3.5%
ValueCountFrequency (%)
1 6550
 
4.7%
2 4868
 
3.5%
3 7181
 
5.1%
4 9331
 
6.7%
5 11017
 
7.9%
6 13174
9.4%
7 15997
11.4%
8 19290
13.8%
9 23316
16.7%
10 29085
20.8%
ValueCountFrequency (%)
10 29085
20.8%
9 23316
16.7%
8 19290
13.8%
7 15997
11.4%
6 13174
9.4%
5 11017
 
7.9%
4 9331
 
6.7%
3 7181
 
5.1%
2 4868
 
3.5%
1 6550
 
4.7%

Trip_distance
Real number (ℝ)

Distinct1108
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean86.499331
Minimum0
Maximum3575
Zeros21
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2024-07-05T13:48:08.681665image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q110
median30
Q373
95-th percentile390
Maximum3575
Range3575
Interquartile range (IQR)63

Descriptive statistics

Standard deviation180.54808
Coefficient of variation (CV)2.0872772
Kurtosis37.458079
Mean86.499331
Median Absolute Deviation (MAD)21
Skewness5.1483495
Sum12093385
Variance32597.609
MonotonicityNot monotonic
2024-07-05T13:48:08.921670image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 13083
 
9.4%
20 10337
 
7.4%
30 7558
 
5.4%
5 7233
 
5.2%
50 6575
 
4.7%
40 5044
 
3.6%
15 4996
 
3.6%
1 3732
 
2.7%
2 3165
 
2.3%
3 3114
 
2.2%
Other values (1098) 74972
53.6%
ValueCountFrequency (%)
0 21
 
< 0.1%
1 3732
2.7%
2 3165
2.3%
3 3114
2.2%
4 2392
 
1.7%
5 7233
5.2%
6 1979
 
1.4%
7 1891
 
1.4%
8 2405
 
1.7%
9 1224
 
0.9%
ValueCountFrequency (%)
3575 1
< 0.1%
3200 1
< 0.1%
3070 1
< 0.1%
3000 1
< 0.1%
2669 1
< 0.1%
2550 1
< 0.1%
2540 1
< 0.1%
2531 1
< 0.1%
2523 2
< 0.1%
2500 2
< 0.1%
Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
1.0
51578 
3.0
42098 
4.0
39867 
2.0
6266 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters419427
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 51578
36.9%
3.0 42098
30.1%
4.0 39867
28.5%
2.0 6266
 
4.5%

Length

2024-07-05T13:48:09.135319image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-05T13:48:09.297701image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 51578
36.9%
3.0 42098
30.1%
4.0 39867
28.5%
2.0 6266
 
4.5%

Most occurring characters

ValueCountFrequency (%)
. 139809
33.3%
0 139809
33.3%
1 51578
 
12.3%
3 42098
 
10.0%
4 39867
 
9.5%
2 6266
 
1.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 279618
66.7%
Other Punctuation 139809
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 139809
50.0%
1 51578
 
18.4%
3 42098
 
15.1%
4 39867
 
14.3%
2 6266
 
2.2%
Other Punctuation
ValueCountFrequency (%)
. 139809
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 419427
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 139809
33.3%
0 139809
33.3%
1 51578
 
12.3%
3 42098
 
10.0%
4 39867
 
9.5%
2 6266
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 419427
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 139809
33.3%
0 139809
33.3%
1 51578
 
12.3%
3 42098
 
10.0%
4 39867
 
9.5%
2 6266
 
1.5%

Trip_starthour
Real number (ℝ)

Distinct29
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.558541
Minimum0
Maximum31
Zeros118
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2024-07-05T13:48:09.482719image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7
Q110
median14
Q317
95-th percentile20
Maximum31
Range31
Interquartile range (IQR)7

Descriptive statistics

Standard deviation4.1761788
Coefficient of variation (CV)0.30801094
Kurtosis-0.62497058
Mean13.558541
Median Absolute Deviation (MAD)3
Skewness0.026156178
Sum1895606
Variance17.440469
MonotonicityNot monotonic
2024-07-05T13:48:09.668144image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
17 11950
 
8.5%
16 11799
 
8.4%
14 11776
 
8.4%
15 11284
 
8.1%
8 11222
 
8.0%
13 10211
 
7.3%
11 9994
 
7.1%
12 9983
 
7.1%
10 9482
 
6.8%
18 7919
 
5.7%
Other values (19) 34189
24.5%
ValueCountFrequency (%)
0 118
 
0.1%
1 206
 
0.1%
2 120
 
0.1%
3 90
 
0.1%
4 108
 
0.1%
5 482
 
0.3%
6 1960
 
1.4%
7 5908
4.2%
8 11222
8.0%
9 7880
5.6%
ValueCountFrequency (%)
31 1
 
< 0.1%
30 1
 
< 0.1%
26 1
 
< 0.1%
25 3
 
< 0.1%
24 40
 
< 0.1%
23 1575
 
1.1%
22 2327
 
1.7%
21 2835
2.0%
20 4121
2.9%
19 6413
4.6%

Part_of_sequence
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
1
120420 
0
19389 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters139809
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
1 120420
86.1%
0 19389
 
13.9%

Length

2024-07-05T13:48:09.851590image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-05T13:48:10.021805image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
1 120420
86.1%
0 19389
 
13.9%

Most occurring characters

ValueCountFrequency (%)
1 120420
86.1%
0 19389
 
13.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 139809
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 120420
86.1%
0 19389
 
13.9%

Most occurring scripts

ValueCountFrequency (%)
Common 139809
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 120420
86.1%
0 19389
 
13.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 139809
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 120420
86.1%
0 19389
 
13.9%

PW_no
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0.0
84376 
1.0
55433 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters419427
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 84376
60.4%
1.0 55433
39.6%

Length

2024-07-05T13:48:10.183376image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-05T13:48:10.336835image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 84376
60.4%
1.0 55433
39.6%

Most occurring characters

ValueCountFrequency (%)
0 224185
53.5%
. 139809
33.3%
1 55433
 
13.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 279618
66.7%
Other Punctuation 139809
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 224185
80.2%
1 55433
 
19.8%
Other Punctuation
ValueCountFrequency (%)
. 139809
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 419427
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 224185
53.5%
. 139809
33.3%
1 55433
 
13.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 419427
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 224185
53.5%
. 139809
33.3%
1 55433
 
13.2%

PW_yeslessthan12h
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0.0
134247 
1.0
 
5562

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters419427
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 134247
96.0%
1.0 5562
 
4.0%

Length

2024-07-05T13:48:10.510505image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-05T13:48:10.652761image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 134247
96.0%
1.0 5562
 
4.0%

Most occurring characters

ValueCountFrequency (%)
0 274056
65.3%
. 139809
33.3%
1 5562
 
1.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 279618
66.7%
Other Punctuation 139809
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 274056
98.0%
1 5562
 
2.0%
Other Punctuation
ValueCountFrequency (%)
. 139809
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 419427
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 274056
65.3%
. 139809
33.3%
1 5562
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 419427
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 274056
65.3%
. 139809
33.3%
1 5562
 
1.3%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0.0
118449 
1.0
21360 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters419427
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 118449
84.7%
1.0 21360
 
15.3%

Length

2024-07-05T13:48:10.815017image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-05T13:48:10.971467image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 118449
84.7%
1.0 21360
 
15.3%

Most occurring characters

ValueCountFrequency (%)
0 258258
61.6%
. 139809
33.3%
1 21360
 
5.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 279618
66.7%
Other Punctuation 139809
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 258258
92.4%
1 21360
 
7.6%
Other Punctuation
ValueCountFrequency (%)
. 139809
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 419427
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 258258
61.6%
. 139809
33.3%
1 21360
 
5.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 419427
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 258258
61.6%
. 139809
33.3%
1 21360
 
5.1%

PW_yesmorethan30h
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0.0
82355 
1.0
57454 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters419427
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0 82355
58.9%
1.0 57454
41.1%

Length

2024-07-05T13:48:11.150052image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-05T13:48:11.312851image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 82355
58.9%
1.0 57454
41.1%

Most occurring characters

ValueCountFrequency (%)
0 222164
53.0%
. 139809
33.3%
1 57454
 
13.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 279618
66.7%
Other Punctuation 139809
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 222164
79.5%
1 57454
 
20.5%
Other Punctuation
ValueCountFrequency (%)
. 139809
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 419427
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 222164
53.0%
. 139809
33.3%
1 57454
 
13.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 419427
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 222164
53.0%
. 139809
33.3%
1 57454
 
13.7%

UO_none
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0.0
70108 
1.0
69701 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters419427
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0 70108
50.1%
1.0 69701
49.9%

Length

2024-07-05T13:48:11.482850image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-05T13:48:11.633134image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 70108
50.1%
1.0 69701
49.9%

Most occurring characters

ValueCountFrequency (%)
0 209917
50.0%
. 139809
33.3%
1 69701
 
16.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 279618
66.7%
Other Punctuation 139809
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 209917
75.1%
1 69701
 
24.9%
Other Punctuation
ValueCountFrequency (%)
. 139809
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 419427
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 209917
50.0%
. 139809
33.3%
1 69701
 
16.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 419427
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 209917
50.0%
. 139809
33.3%
1 69701
 
16.6%

UO_benefits
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0.0
96981 
1.0
42828 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters419427
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 96981
69.4%
1.0 42828
30.6%

Length

2024-07-05T13:48:11.807382image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-05T13:48:11.971611image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 96981
69.4%
1.0 42828
30.6%

Most occurring characters

ValueCountFrequency (%)
0 236790
56.5%
. 139809
33.3%
1 42828
 
10.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 279618
66.7%
Other Punctuation 139809
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 236790
84.7%
1 42828
 
15.3%
Other Punctuation
ValueCountFrequency (%)
. 139809
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 419427
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 236790
56.5%
. 139809
33.3%
1 42828
 
10.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 419427
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 236790
56.5%
. 139809
33.3%
1 42828
 
10.2%

UO_student/scholar
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0.0
112529 
1.0
27280 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters419427
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 112529
80.5%
1.0 27280
 
19.5%

Length

2024-07-05T13:48:12.145667image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-05T13:48:12.309716image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 112529
80.5%
1.0 27280
 
19.5%

Most occurring characters

ValueCountFrequency (%)
0 252338
60.2%
. 139809
33.3%
1 27280
 
6.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 279618
66.7%
Other Punctuation 139809
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 252338
90.2%
1 27280
 
9.8%
Other Punctuation
ValueCountFrequency (%)
. 139809
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 419427
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 252338
60.2%
. 139809
33.3%
1 27280
 
6.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 419427
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 252338
60.2%
. 139809
33.3%
1 27280
 
6.5%

HHC_oneperson
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0.0
114158 
1.0
25651 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters419427
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 114158
81.7%
1.0 25651
 
18.3%

Length

2024-07-05T13:48:12.483013image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-05T13:48:12.636186image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 114158
81.7%
1.0 25651
 
18.3%

Most occurring characters

ValueCountFrequency (%)
0 253967
60.6%
. 139809
33.3%
1 25651
 
6.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 279618
66.7%
Other Punctuation 139809
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 253967
90.8%
1 25651
 
9.2%
Other Punctuation
ValueCountFrequency (%)
. 139809
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 419427
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 253967
60.6%
. 139809
33.3%
1 25651
 
6.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 419427
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 253967
60.6%
. 139809
33.3%
1 25651
 
6.1%

HHC_couple
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0.0
97454 
1.0
42355 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters419427
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0 97454
69.7%
1.0 42355
30.3%

Length

2024-07-05T13:48:12.809271image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-05T13:48:12.971565image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 97454
69.7%
1.0 42355
30.3%

Most occurring characters

ValueCountFrequency (%)
0 237263
56.6%
. 139809
33.3%
1 42355
 
10.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 279618
66.7%
Other Punctuation 139809
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 237263
84.9%
1 42355
 
15.1%
Other Punctuation
ValueCountFrequency (%)
. 139809
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 419427
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 237263
56.6%
. 139809
33.3%
1 42355
 
10.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 419427
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 237263
56.6%
. 139809
33.3%
1 42355
 
10.1%

HHC_couple_with_children
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0.0
76989 
1.0
62820 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters419427
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 76989
55.1%
1.0 62820
44.9%

Length

2024-07-05T13:48:13.138431image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-05T13:48:13.301018image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 76989
55.1%
1.0 62820
44.9%

Most occurring characters

ValueCountFrequency (%)
0 216798
51.7%
. 139809
33.3%
1 62820
 
15.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 279618
66.7%
Other Punctuation 139809
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 216798
77.5%
1 62820
 
22.5%
Other Punctuation
ValueCountFrequency (%)
. 139809
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 419427
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 216798
51.7%
. 139809
33.3%
1 62820
 
15.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 419427
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 216798
51.7%
. 139809
33.3%
1 62820
 
15.0%

HHC_oneperson_with_children
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0.0
130826 
1.0
 
8983

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters419427
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 130826
93.6%
1.0 8983
 
6.4%

Length

2024-07-05T13:48:13.473990image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-05T13:48:13.630931image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 130826
93.6%
1.0 8983
 
6.4%

Most occurring characters

ValueCountFrequency (%)
0 270635
64.5%
. 139809
33.3%
1 8983
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 279618
66.7%
Other Punctuation 139809
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 270635
96.8%
1 8983
 
3.2%
Other Punctuation
ValueCountFrequency (%)
. 139809
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 419427
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 270635
64.5%
. 139809
33.3%
1 8983
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 419427
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 270635
64.5%
. 139809
33.3%
1 8983
 
2.1%

Ethn_dutch
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
1.0
110847 
0.0
28962 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters419427
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
1.0 110847
79.3%
0.0 28962
 
20.7%

Length

2024-07-05T13:48:13.786953image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-05T13:48:13.954007image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 110847
79.3%
0.0 28962
 
20.7%

Most occurring characters

ValueCountFrequency (%)
0 168771
40.2%
. 139809
33.3%
1 110847
26.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 279618
66.7%
Other Punctuation 139809
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 168771
60.4%
1 110847
39.6%
Other Punctuation
ValueCountFrequency (%)
. 139809
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 419427
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 168771
40.2%
. 139809
33.3%
1 110847
26.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 419427
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 168771
40.2%
. 139809
33.3%
1 110847
26.4%

Ethn_western
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0.0
125529 
1.0
14280 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters419427
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0 125529
89.8%
1.0 14280
 
10.2%

Length

2024-07-05T13:48:14.114535image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-05T13:48:14.265579image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 125529
89.8%
1.0 14280
 
10.2%

Most occurring characters

ValueCountFrequency (%)
0 265338
63.3%
. 139809
33.3%
1 14280
 
3.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 279618
66.7%
Other Punctuation 139809
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 265338
94.9%
1 14280
 
5.1%
Other Punctuation
ValueCountFrequency (%)
. 139809
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 419427
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 265338
63.3%
. 139809
33.3%
1 14280
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 419427
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 265338
63.3%
. 139809
33.3%
1 14280
 
3.4%

Ethn_nonwestern
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0.0
125127 
1.0
14682 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters419427
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 125127
89.5%
1.0 14682
 
10.5%

Length

2024-07-05T13:48:14.443580image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-05T13:48:14.597204image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 125127
89.5%
1.0 14682
 
10.5%

Most occurring characters

ValueCountFrequency (%)
0 264936
63.2%
. 139809
33.3%
1 14682
 
3.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 279618
66.7%
Other Punctuation 139809
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 264936
94.7%
1 14682
 
5.3%
Other Punctuation
ValueCountFrequency (%)
. 139809
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 419427
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 264936
63.2%
. 139809
33.3%
1 14682
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 419427
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 264936
63.2%
. 139809
33.3%
1 14682
 
3.5%

EL_primary
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0.0
121161 
1.0
18648 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters419427
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0 121161
86.7%
1.0 18648
 
13.3%

Length

2024-07-05T13:48:14.762752image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-05T13:48:14.915121image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 121161
86.7%
1.0 18648
 
13.3%

Most occurring characters

ValueCountFrequency (%)
0 260970
62.2%
. 139809
33.3%
1 18648
 
4.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 279618
66.7%
Other Punctuation 139809
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 260970
93.3%
1 18648
 
6.7%
Other Punctuation
ValueCountFrequency (%)
. 139809
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 419427
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 260970
62.2%
. 139809
33.3%
1 18648
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 419427
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 260970
62.2%
. 139809
33.3%
1 18648
 
4.4%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0.0
122686 
1.0
17123 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters419427
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 122686
87.8%
1.0 17123
 
12.2%

Length

2024-07-05T13:48:15.088117image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-05T13:48:15.247299image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 122686
87.8%
1.0 17123
 
12.2%

Most occurring characters

ValueCountFrequency (%)
0 262495
62.6%
. 139809
33.3%
1 17123
 
4.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 279618
66.7%
Other Punctuation 139809
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 262495
93.9%
1 17123
 
6.1%
Other Punctuation
ValueCountFrequency (%)
. 139809
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 419427
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 262495
62.6%
. 139809
33.3%
1 17123
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 419427
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 262495
62.6%
. 139809
33.3%
1 17123
 
4.1%

EL_secondary_higher
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0.0
98535 
1.0
41274 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters419427
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 98535
70.5%
1.0 41274
29.5%

Length

2024-07-05T13:48:15.426475image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-05T13:48:15.578780image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 98535
70.5%
1.0 41274
29.5%

Most occurring characters

ValueCountFrequency (%)
0 238344
56.8%
. 139809
33.3%
1 41274
 
9.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 279618
66.7%
Other Punctuation 139809
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 238344
85.2%
1 41274
 
14.8%
Other Punctuation
ValueCountFrequency (%)
. 139809
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 419427
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 238344
56.8%
. 139809
33.3%
1 41274
 
9.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 419427
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 238344
56.8%
. 139809
33.3%
1 41274
 
9.8%

EL_higherprofessional_university
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0.0
77045 
1.0
62764 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters419427
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 77045
55.1%
1.0 62764
44.9%

Length

2024-07-05T13:48:15.749525image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-05T13:48:15.898376image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 77045
55.1%
1.0 62764
44.9%

Most occurring characters

ValueCountFrequency (%)
0 216854
51.7%
. 139809
33.3%
1 62764
 
15.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 279618
66.7%
Other Punctuation 139809
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 216854
77.6%
1 62764
 
22.4%
Other Punctuation
ValueCountFrequency (%)
. 139809
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 419427
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 216854
51.7%
. 139809
33.3%
1 62764
 
15.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 419427
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 216854
51.7%
. 139809
33.3%
1 62764
 
15.0%

Moti_work
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0.0
114763 
1.0
25046 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters419427
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0 114763
82.1%
1.0 25046
 
17.9%

Length

2024-07-05T13:48:16.088379image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-05T13:48:16.248926image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 114763
82.1%
1.0 25046
 
17.9%

Most occurring characters

ValueCountFrequency (%)
0 254572
60.7%
. 139809
33.3%
1 25046
 
6.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 279618
66.7%
Other Punctuation 139809
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 254572
91.0%
1 25046
 
9.0%
Other Punctuation
ValueCountFrequency (%)
. 139809
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 419427
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 254572
60.7%
. 139809
33.3%
1 25046
 
6.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 419427
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 254572
60.7%
. 139809
33.3%
1 25046
 
6.0%

Moti_profession
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0.0
137842 
1.0
 
1967

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters419427
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row1.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 137842
98.6%
1.0 1967
 
1.4%

Length

2024-07-05T13:48:16.414336image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-05T13:48:16.580309image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 137842
98.6%
1.0 1967
 
1.4%

Most occurring characters

ValueCountFrequency (%)
0 277651
66.2%
. 139809
33.3%
1 1967
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 279618
66.7%
Other Punctuation 139809
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 277651
99.3%
1 1967
 
0.7%
Other Punctuation
ValueCountFrequency (%)
. 139809
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 419427
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 277651
66.2%
. 139809
33.3%
1 1967
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 419427
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 277651
66.2%
. 139809
33.3%
1 1967
 
0.5%

Moti_pickupdropoff_person
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0.0
130777 
1.0
 
9032

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters419427
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 130777
93.5%
1.0 9032
 
6.5%

Length

2024-07-05T13:48:16.749103image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-05T13:48:16.911606image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 130777
93.5%
1.0 9032
 
6.5%

Most occurring characters

ValueCountFrequency (%)
0 270586
64.5%
. 139809
33.3%
1 9032
 
2.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 279618
66.7%
Other Punctuation 139809
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 270586
96.8%
1 9032
 
3.2%
Other Punctuation
ValueCountFrequency (%)
. 139809
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 419427
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 270586
64.5%
. 139809
33.3%
1 9032
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 419427
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 270586
64.5%
. 139809
33.3%
1 9032
 
2.2%

Moti_pickupdropoff_goods
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0.0
99079 
1.0
40730 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters419427
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 99079
70.9%
1.0 40730
29.1%

Length

2024-07-05T13:48:17.071543image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-05T13:48:17.236312image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 99079
70.9%
1.0 40730
29.1%

Most occurring characters

ValueCountFrequency (%)
0 238888
57.0%
. 139809
33.3%
1 40730
 
9.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 279618
66.7%
Other Punctuation 139809
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 238888
85.4%
1 40730
 
14.6%
Other Punctuation
ValueCountFrequency (%)
. 139809
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 419427
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 238888
57.0%
. 139809
33.3%
1 40730
 
9.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 419427
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 238888
57.0%
. 139809
33.3%
1 40730
 
9.7%

Moti_sparetime
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0.0
76775 
1.0
63034 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters419427
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 76775
54.9%
1.0 63034
45.1%

Length

2024-07-05T13:48:17.397954image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-05T13:48:17.580703image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 76775
54.9%
1.0 63034
45.1%

Most occurring characters

ValueCountFrequency (%)
0 216584
51.6%
. 139809
33.3%
1 63034
 
15.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 279618
66.7%
Other Punctuation 139809
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 216584
77.5%
1 63034
 
22.5%
Other Punctuation
ValueCountFrequency (%)
. 139809
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 419427
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 216584
51.6%
. 139809
33.3%
1 63034
 
15.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 419427
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 216584
51.6%
. 139809
33.3%
1 63034
 
15.0%

Main_moti_work
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0.0
108207 
1.0
31602 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters419427
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 108207
77.4%
1.0 31602
 
22.6%

Length

2024-07-05T13:48:17.750506image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-05T13:48:17.915985image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 108207
77.4%
1.0 31602
 
22.6%

Most occurring characters

ValueCountFrequency (%)
0 248016
59.1%
. 139809
33.3%
1 31602
 
7.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 279618
66.7%
Other Punctuation 139809
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 248016
88.7%
1 31602
 
11.3%
Other Punctuation
ValueCountFrequency (%)
. 139809
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 419427
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 248016
59.1%
. 139809
33.3%
1 31602
 
7.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 419427
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 248016
59.1%
. 139809
33.3%
1 31602
 
7.5%

Main_moti_profession
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0.0
135857 
1.0
 
3952

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters419427
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 135857
97.2%
1.0 3952
 
2.8%

Length

2024-07-05T13:48:18.088227image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-05T13:48:18.250856image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 135857
97.2%
1.0 3952
 
2.8%

Most occurring characters

ValueCountFrequency (%)
0 275666
65.7%
. 139809
33.3%
1 3952
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 279618
66.7%
Other Punctuation 139809
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 275666
98.6%
1 3952
 
1.4%
Other Punctuation
ValueCountFrequency (%)
. 139809
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 419427
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 275666
65.7%
. 139809
33.3%
1 3952
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 419427
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 275666
65.7%
. 139809
33.3%
1 3952
 
0.9%

Main_moti_pickupdropoff_person
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0.0
133404 
1.0
 
6405

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters419427
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 133404
95.4%
1.0 6405
 
4.6%

Length

2024-07-05T13:48:18.967626image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-05T13:48:19.121753image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 133404
95.4%
1.0 6405
 
4.6%

Most occurring characters

ValueCountFrequency (%)
0 273213
65.1%
. 139809
33.3%
1 6405
 
1.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 279618
66.7%
Other Punctuation 139809
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 273213
97.7%
1 6405
 
2.3%
Other Punctuation
ValueCountFrequency (%)
. 139809
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 419427
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 273213
65.1%
. 139809
33.3%
1 6405
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 419427
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 273213
65.1%
. 139809
33.3%
1 6405
 
1.5%

Main_moti_pickupdropoff_goods
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0.0
95280 
1.0
44529 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters419427
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0 95280
68.2%
1.0 44529
31.8%

Length

2024-07-05T13:48:19.281609image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-05T13:48:19.444044image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 95280
68.2%
1.0 44529
31.8%

Most occurring characters

ValueCountFrequency (%)
0 235089
56.1%
. 139809
33.3%
1 44529
 
10.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 279618
66.7%
Other Punctuation 139809
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 235089
84.1%
1 44529
 
15.9%
Other Punctuation
ValueCountFrequency (%)
. 139809
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 419427
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 235089
56.1%
. 139809
33.3%
1 44529
 
10.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 419427
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 235089
56.1%
. 139809
33.3%
1 44529
 
10.6%

Main_moti_sparetime
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0.0
86488 
1.0
53321 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters419427
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 86488
61.9%
1.0 53321
38.1%

Length

2024-07-05T13:48:19.617062image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-05T13:48:19.753130image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 86488
61.9%
1.0 53321
38.1%

Most occurring characters

ValueCountFrequency (%)
0 226297
54.0%
. 139809
33.3%
1 53321
 
12.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 279618
66.7%
Other Punctuation 139809
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 226297
80.9%
1 53321
 
19.1%
Other Punctuation
ValueCountFrequency (%)
. 139809
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 419427
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 226297
54.0%
. 139809
33.3%
1 53321
 
12.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 419427
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 226297
54.0%
. 139809
33.3%
1 53321
 
12.7%

Interactions

2024-07-05T13:48:02.378271image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T13:47:55.748297image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T13:47:57.563023image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T13:47:58.812859image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T13:47:59.912519image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T13:48:01.157457image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T13:48:02.565943image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T13:47:55.955156image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T13:47:57.740790image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T13:47:59.004781image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T13:48:00.095448image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T13:48:01.411695image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T13:48:02.738396image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T13:47:56.162750image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T13:47:57.904761image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T13:47:59.172726image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T13:48:00.284007image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T13:48:01.594967image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T13:48:02.928070image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T13:47:56.974151image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T13:47:58.083413image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T13:47:59.345894image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T13:48:00.463052image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T13:48:01.796256image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T13:48:03.115305image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T13:47:57.190428image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T13:47:58.299506image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T13:47:59.529051image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T13:48:00.645731image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T13:48:01.994968image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T13:48:03.294410image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T13:47:57.382592image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T13:47:58.635036image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T13:47:59.715393image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T13:48:00.845465image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T13:48:02.178334image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Correlations

2024-07-05T13:48:19.946880image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
AgeDisposable_income_householdEL_higherprofessional_universityEL_primaryEL_secondary_higherEL_secondary_lowerEbike_in_HHEthn_dutchEthn_nonwesternEthn_westernGender_maleHHC_coupleHHC_couple_with_childrenHHC_onepersonHHC_oneperson_with_childrenMain_moti_pickupdropoff_goodsMain_moti_pickupdropoff_personMain_moti_professionMain_moti_sparetimeMain_moti_workMoti_pickupdropoff_goodsMoti_pickupdropoff_personMoti_professionMoti_sparetimeMoti_workNumber_of_cars_in_HHPW_noPW_yeslessthan12hPW_yesmorethan12to30hPW_yesmorethan30hPart_of_sequenceStarting_postalcodeTrip_distanceTrip_starthourTrip_transportation_typeUO_benefitsUO_noneUO_student/scholar
Age1.000-0.1440.3870.7650.2640.2950.2560.1400.1650.0490.0950.5140.5180.2130.1680.0560.0750.0170.0950.0590.1810.2100.0530.2900.212-0.0240.6880.2310.2300.5560.0590.0410.093-0.0690.2110.6650.6510.877
Disposable_income_household-0.1441.0000.1760.0510.0730.2090.1090.0980.0820.0550.0490.1760.4540.4940.1460.0220.0180.0150.0300.0200.0840.0670.0190.0320.0460.4340.2220.0440.0920.1840.029-0.0450.0580.0090.0780.2800.2080.187
EL_higherprofessional_university0.3870.1761.0000.3540.5840.3370.0840.0000.0380.0350.0110.0560.0630.0570.0670.0150.0220.0200.0260.0100.0250.0640.0120.0840.042-0.0260.2710.0660.0160.2840.007-0.0750.0410.0380.0650.0710.2970.292
EL_primary0.7650.0510.3541.0000.2540.1470.0290.0950.1070.0180.0170.1620.1970.1050.0700.0230.0200.0040.0610.0330.0910.0600.0230.2080.116-0.0050.3530.0110.1310.2600.015-0.019-0.134-0.0210.2390.1060.3100.514
EL_secondary_higher0.2640.0730.5840.2541.0000.2420.0300.0320.0160.0270.0020.0310.0100.0050.0290.0090.0000.0150.0110.0170.0120.0000.0260.0580.0530.0630.0780.0390.0980.0100.0030.0600.0500.0030.1020.0150.0220.010
EL_secondary_lower0.2950.2090.3370.1470.2421.0000.1160.0500.0320.0340.0040.1260.1230.0140.0100.0150.0130.0140.0080.0060.0410.0350.0060.0070.017-0.0420.1540.0340.0260.1470.0000.0500.007-0.0400.0150.2380.1600.075
Ebike_in_HH0.2560.1090.0840.0290.0300.1161.0000.1440.1260.0640.0130.1460.0000.1340.0660.0030.0000.0070.0070.0050.0060.0000.0010.0150.0280.1200.0980.0050.0090.1070.0180.0840.048-0.0300.0840.1550.0900.067
Ethn_dutch0.1400.0980.0000.0950.0320.0500.1441.0000.6700.6600.0110.0670.0050.0330.0620.0000.0080.0120.0000.0000.0010.0040.0030.0120.0100.1530.0130.0040.0400.0130.0440.1570.033-0.0060.1240.0470.0120.070
Ethn_nonwestern0.1650.0820.0380.1070.0160.0320.1260.6701.0000.1150.0050.0970.0490.0000.0780.0000.0080.0080.0020.0040.0070.0070.0030.0110.021-0.1150.0090.0130.0270.0040.047-0.137-0.0250.0050.1210.0680.0180.101
Ethn_western0.0490.0550.0350.0180.0270.0340.0640.6600.1151.0000.0200.0090.0420.0410.0020.0000.0000.0070.0060.0060.0110.0000.0000.0040.006-0.0880.0080.0060.0250.0130.012-0.072-0.0190.0040.0470.0060.0000.009
Gender_male0.0950.0490.0110.0170.0020.0040.0130.0110.0050.0201.0000.0580.0040.0130.0790.0130.0040.0100.0050.0060.0230.0490.0190.0090.0420.0200.0040.0740.2780.2280.0000.0040.1050.0050.0930.0220.0410.025
HHC_couple0.5140.1760.0560.1620.0310.1260.1460.0670.0970.0090.0581.0000.5950.3120.1730.0300.0330.0100.0170.0000.0860.0940.0050.0230.010-0.0390.1050.0400.0770.0320.0020.0170.053-0.0210.0720.2930.0610.265
HHC_couple_with_children0.5180.4540.0630.1970.0100.1230.0000.0050.0490.0420.0040.5951.0000.4280.2370.0340.0490.0020.0110.0000.1150.1420.0140.0160.0180.4000.1300.0520.1060.0310.0260.026-0.017-0.0180.0720.2940.0900.229
HHC_oneperson0.2130.4940.0570.1050.0050.0140.1340.0330.0000.0410.0130.3120.4281.0000.1240.0100.0180.0020.0010.0030.0550.0820.0080.0050.002-0.4030.0330.0320.0660.0280.024-0.037-0.0270.0430.0850.0720.0200.059
HHC_oneperson_with_children0.1680.1460.0670.0700.0290.0100.0660.0620.0780.0020.0790.1730.2370.1241.0000.0020.0070.0050.0050.0030.0160.0170.0040.0170.013-0.1010.0140.0190.0340.0460.007-0.026-0.0230.0090.0360.0670.0360.125
Main_moti_pickupdropoff_goods0.0560.0220.0150.0230.0090.0150.0030.0000.0000.0000.0130.0300.0340.0100.0021.0000.1500.1170.5370.3690.1610.0310.0320.0670.074-0.0200.0110.0100.0060.0120.0020.012-0.033-0.0360.0420.0380.0000.045
Main_moti_pickupdropoff_person0.0750.0180.0220.0200.0000.0130.0000.0080.0080.0000.0040.0330.0490.0180.0070.1501.0000.0370.1720.1180.0240.1720.0020.0420.0250.0080.0230.0090.0190.0120.012-0.009-0.004-0.0190.0310.0070.0180.031
Main_moti_profession0.0170.0150.0200.0040.0150.0140.0070.0120.0080.0070.0100.0100.0020.0020.0050.1170.0371.0000.1340.0920.0190.0090.1790.0140.0080.0140.0180.0020.0070.0100.0000.0180.020-0.0030.0250.0000.0160.019
Main_moti_sparetime0.0950.0300.0260.0610.0110.0080.0070.0000.0020.0060.0050.0170.0110.0010.0050.5370.1720.1341.0000.4240.0770.0270.0240.1470.0750.0020.0720.0130.0300.0550.000-0.010-0.0060.0110.0350.0000.0720.088
Main_moti_work0.0590.0200.0100.0330.0170.0060.0050.0000.0040.0060.0060.0000.0000.0030.0030.3690.1180.0920.4241.0000.0710.0150.0050.0690.1860.0090.0780.0000.0150.0670.000-0.0040.0380.0380.0420.0480.0670.029
Moti_pickupdropoff_goods0.1810.0840.0250.0910.0120.0410.0060.0010.0070.0110.0230.0860.1150.0550.0160.1610.0240.0190.0770.0711.0000.1680.0760.5810.299-0.0610.0500.0180.0000.0430.078-0.000-0.235-0.0100.0850.1470.0340.128
Moti_pickupdropoff_person0.2100.0670.0640.0600.0000.0350.0000.0040.0070.0000.0490.0940.1420.0820.0170.0310.1720.0090.0270.0150.1681.0000.0310.2380.1230.0490.0690.0020.0750.0150.048-0.002-0.033-0.0250.1200.0110.0580.087
Moti_profession0.0530.0190.0120.0230.0260.0060.0010.0030.0030.0000.0190.0050.0140.0080.0040.0320.0020.1790.0240.0050.0760.0311.0000.1080.0560.0340.0710.0080.0320.0440.0250.0110.0540.0030.0740.0330.0580.035
Moti_sparetime0.2900.0320.0840.2080.0580.0070.0150.0120.0110.0040.0090.0230.0160.0050.0170.0670.0420.0140.1470.0690.5810.2380.1081.0000.423-0.0080.2140.0230.0880.1570.0430.0150.0390.1310.2600.0000.1960.250
Moti_work0.2120.0460.0420.1160.0530.0170.0280.0100.0210.0060.0420.0100.0180.0020.0130.0740.0250.0080.0750.1860.2990.1230.0560.4231.0000.0400.2710.0080.0570.2310.075-0.0220.234-0.1430.2090.1680.2400.107
Number_of_cars_in_HH-0.0240.434-0.026-0.0050.063-0.0420.1200.153-0.115-0.0880.020-0.0390.400-0.403-0.101-0.0200.0080.0140.0020.009-0.0610.0490.034-0.0080.0401.0000.1580.0270.0810.0900.0340.1250.139-0.0120.1210.1610.1270.056
PW_no0.6880.2220.2710.3530.0780.1540.0980.0130.0090.0080.0040.1050.1300.0330.0140.0110.0230.0180.0720.0780.0500.0690.0710.2140.2710.1581.0000.1650.3440.6770.0150.019-0.138-0.0350.2130.5520.8080.377
PW_yeslessthan12h0.2310.0440.0660.0110.0390.0340.0050.0040.0130.0060.0740.0400.0520.0320.0190.0100.0090.0020.0130.0000.0180.0020.0080.0230.0080.0270.1651.0000.0860.1700.0070.009-0.0240.0090.0720.0230.1230.182
PW_yesmorethan12to30h0.2300.0920.0160.1310.0980.0260.0090.0400.0270.0250.2780.0770.1060.0660.0340.0060.0190.0070.0300.0150.0000.0750.0320.0880.0570.0810.3440.0861.0000.3550.0200.050-0.005-0.0120.0560.1210.1880.096
PW_yesmorethan30h0.5560.1840.2840.2600.0100.1470.1070.0130.0040.0130.2280.0320.0310.0280.0460.0120.0120.0100.0550.0670.0430.0150.0440.1570.2310.0900.6770.1700.3551.0000.002-0.0590.1500.0400.1990.4510.7150.377
Part_of_sequence0.0590.0290.0070.0150.0030.0000.0180.0440.0470.0120.0000.0020.0260.0240.0070.0020.0120.0000.0000.0000.0780.0480.0250.0430.0750.0340.0150.0070.0200.0021.0000.027-0.0500.3080.1520.0130.0170.037
Starting_postalcode0.041-0.045-0.075-0.0190.0600.0500.0840.157-0.137-0.0720.0040.0170.026-0.037-0.0260.012-0.0090.018-0.010-0.004-0.000-0.0020.0110.015-0.0220.1250.0190.0090.050-0.0590.0271.0000.046-0.0130.0930.0580.0360.047
Trip_distance0.0930.0580.041-0.1340.0500.0070.0480.033-0.025-0.0190.1050.053-0.017-0.027-0.023-0.033-0.0040.020-0.0060.038-0.235-0.0330.0540.0390.2340.139-0.138-0.024-0.0050.150-0.0500.0461.000-0.0480.1580.0510.0930.058
Trip_starthour-0.0690.0090.038-0.0210.003-0.040-0.030-0.0060.0050.0040.005-0.021-0.0180.0430.009-0.036-0.019-0.0030.0110.038-0.010-0.0250.0030.131-0.143-0.012-0.0350.009-0.0120.0400.308-0.013-0.0481.0000.0630.1680.1510.083
Trip_transportation_type0.2110.0780.0650.2390.1020.0150.0840.1240.1210.0470.0930.0720.0720.0850.0360.0420.0310.0250.0350.0420.0850.1200.0740.2600.2090.1210.2130.0720.0560.1990.1520.0930.1580.0631.0000.0930.2130.309
UO_benefits0.6650.2800.0710.1060.0150.2380.1550.0470.0680.0060.0220.2930.2940.0720.0670.0380.0070.0000.0000.0480.1470.0110.0330.0000.1680.1610.5520.0230.1210.4510.0130.0580.0510.1680.0931.0000.6630.327
UO_none0.6510.2080.2970.3100.0220.1600.0900.0120.0180.0000.0410.0610.0900.0200.0360.0000.0180.0160.0720.0670.0340.0580.0580.1960.2400.1270.8080.1230.1880.7150.0170.0360.0930.1510.2130.6631.0000.491
UO_student/scholar0.8770.1870.2920.5140.0100.0750.0670.0700.1010.0090.0250.2650.2290.0590.1250.0450.0310.0190.0880.0290.1280.0870.0350.2500.1070.0560.3770.1820.0960.3770.0370.0470.0580.0830.3090.3270.4911.000

Missing values

2024-07-05T13:48:03.824265image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
A simple visualization of nullity by column.
2024-07-05T13:48:04.943768image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Starting_postalcodeGender_maleAgeNumber_of_cars_in_HHEbike_in_HHDisposable_income_householdTrip_distanceTrip_transportation_typeTrip_starthourPart_of_sequencePW_noPW_yeslessthan12hPW_yesmorethan12to30hPW_yesmorethan30hUO_noneUO_benefitsUO_student/scholarHHC_onepersonHHC_coupleHHC_couple_with_childrenHHC_oneperson_with_childrenEthn_dutchEthn_westernEthn_nonwesternEL_primaryEL_secondary_lowerEL_secondary_higherEL_higherprofessional_universityMoti_workMoti_professionMoti_pickupdropoff_personMoti_pickupdropoff_goodsMoti_sparetimeMain_moti_workMain_moti_professionMain_moti_pickupdropoff_personMain_moti_pickupdropoff_goodsMain_moti_sparetime
09161.0143209100.01.011.010.00.00.01.01.00.00.00.00.01.00.01.00.00.00.00.01.00.00.00.01.00.00.00.00.00.01.00.0
19163.0143209100.01.011.010.00.00.01.01.00.00.00.00.01.00.01.00.00.00.00.01.00.00.00.00.00.01.00.00.00.01.00.0
29161.0143209150.01.014.010.00.00.01.01.00.00.00.00.01.00.01.00.00.00.00.01.00.00.01.00.00.00.00.00.00.01.00.0
3NaN143209150.01.021.010.00.00.01.01.00.00.00.00.01.00.01.00.00.00.00.01.00.00.01.00.00.00.00.00.00.01.00.0
4NaN13720101600.01.05.000.00.00.01.01.00.00.00.01.00.00.00.01.00.01.00.00.00.01.00.00.00.00.00.00.00.01.00.0
57418.01372010400.01.017.010.00.00.01.01.00.00.00.01.00.00.00.01.00.01.00.00.00.01.00.00.00.00.01.00.00.00.00.0
68181.007311410.03.09.001.00.00.00.00.01.00.01.00.00.00.01.00.00.00.01.00.00.00.00.00.00.01.01.00.00.00.00.0
78181.007311410.03.011.011.00.00.00.00.01.00.01.00.00.00.01.00.00.00.01.00.00.00.00.00.00.01.00.00.00.00.01.0
86663.008101010.03.013.011.00.00.00.00.00.01.00.00.00.01.01.00.00.01.00.00.00.00.00.00.00.01.00.00.00.00.01.0
93882.0130218280.01.06.010.00.00.01.00.01.00.00.01.00.00.01.00.00.00.00.01.00.01.00.00.00.00.00.00.00.00.01.0
Starting_postalcodeGender_maleAgeNumber_of_cars_in_HHEbike_in_HHDisposable_income_householdTrip_distanceTrip_transportation_typeTrip_starthourPart_of_sequencePW_noPW_yeslessthan12hPW_yesmorethan12to30hPW_yesmorethan30hUO_noneUO_benefitsUO_student/scholarHHC_onepersonHHC_coupleHHC_couple_with_childrenHHC_oneperson_with_childrenEthn_dutchEthn_westernEthn_nonwesternEL_primaryEL_secondary_lowerEL_secondary_higherEL_higherprofessional_universityMoti_workMoti_professionMoti_pickupdropoff_personMoti_pickupdropoff_goodsMoti_sparetimeMain_moti_workMain_moti_professionMain_moti_pickupdropoff_personMain_moti_pickupdropoff_goodsMain_moti_sparetime
1397992905.003100810.04.013.000.00.00.01.01.00.00.00.01.00.00.00.00.01.01.00.00.00.00.00.00.00.01.00.00.00.01.00.0
1398002991.00322079.01.09.000.00.01.00.01.00.00.00.00.01.00.01.00.00.00.01.00.00.00.00.00.01.00.00.00.00.01.00.0
1398012991.00322079.01.010.010.00.01.00.01.00.00.00.00.01.00.01.00.00.00.01.00.00.00.00.00.01.00.00.00.00.01.00.0
1398023721.005131940.04.09.000.00.01.00.01.00.00.00.01.00.00.01.00.00.00.00.01.00.00.00.00.00.01.00.00.00.01.00.0
1398033721.00513195.04.014.010.00.01.00.01.00.00.00.01.00.00.01.00.00.00.00.01.00.00.00.00.01.00.00.00.00.01.00.0
1398043722.00513195.04.014.010.00.01.00.01.00.00.00.01.00.00.01.00.00.00.00.01.00.00.00.00.01.00.00.00.00.01.00.0
1398053573.004310930.03.08.000.00.00.01.01.00.00.00.00.01.00.00.01.00.00.00.00.01.01.00.00.00.00.00.00.00.01.00.0
1398063515.004310930.03.017.010.00.00.01.01.00.00.00.00.01.00.00.01.00.00.00.00.01.01.00.00.00.00.01.00.00.00.00.0
1398073573.00431093.04.017.010.00.00.01.01.00.00.00.00.01.00.00.01.00.00.00.00.01.00.00.00.00.01.01.00.00.00.00.0
1398083451.0079112100.03.013.001.00.00.00.00.01.00.01.00.00.00.01.00.00.00.01.00.00.00.00.00.00.01.01.00.00.00.00.0

Duplicate rows

Most frequently occurring

Starting_postalcodeGender_maleAgeNumber_of_cars_in_HHEbike_in_HHDisposable_income_householdTrip_distanceTrip_transportation_typeTrip_starthourPart_of_sequencePW_noPW_yeslessthan12hPW_yesmorethan12to30hPW_yesmorethan30hUO_noneUO_benefitsUO_student/scholarHHC_onepersonHHC_coupleHHC_couple_with_childrenHHC_oneperson_with_childrenEthn_dutchEthn_westernEthn_nonwesternEL_primaryEL_secondary_lowerEL_secondary_higherEL_higherprofessional_universityMoti_workMoti_professionMoti_pickupdropoff_personMoti_pickupdropoff_goodsMoti_sparetimeMain_moti_workMain_moti_professionMain_moti_pickupdropoff_personMain_moti_pickupdropoff_goodsMain_moti_sparetime# duplicates
20143434.01751031.04.011.001.00.00.00.00.01.00.01.00.00.00.01.00.00.01.00.00.00.00.00.00.01.00.00.00.00.00.01.08
10942526.01461071.01.011.010.00.00.01.01.00.00.00.00.01.00.00.00.01.00.00.00.01.00.00.00.00.01.00.00.00.01.00.07
25943903.01500038.01.010.000.00.00.01.01.00.00.00.01.00.00.01.00.00.00.00.01.00.00.00.00.00.01.00.00.00.00.01.06
5051619.00603091.04.011.000.00.01.00.00.01.00.00.00.01.00.01.00.00.00.00.00.01.00.00.00.01.00.01.00.00.00.00.05
23033641.00762097.01.010.001.00.00.00.00.01.00.00.01.00.00.01.00.00.00.00.01.00.00.00.00.00.01.00.00.00.00.01.05
26723945.01141056.03.08.001.00.00.00.00.00.01.00.00.01.00.00.00.01.01.00.00.00.00.00.00.00.01.00.00.00.01.00.05
28544386.0072091.04.08.001.00.00.00.00.00.01.00.00.01.00.01.00.00.01.00.00.00.00.00.00.00.01.01.00.00.00.00.05
28804511.00152195.04.017.001.00.00.00.00.00.01.00.00.01.00.00.01.00.00.01.00.00.00.00.00.00.01.01.00.00.00.00.05
41617206.01111065.03.08.001.00.00.00.00.00.01.00.00.01.00.00.00.01.01.00.00.00.00.00.00.00.01.00.00.00.00.01.05
2671185.00493067.01.012.011.00.00.00.00.01.00.00.00.01.00.00.00.01.00.00.00.01.00.00.01.00.00.00.00.00.01.00.04